Cited by 1,129Advanced Causal Inference TechniquesStatistical Methods in EpidemiologyMeta-analysis and systematic reviews

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Triangulation in aetiological epidemiology

Open access

Lawlor, Debbie A., Tilling, Kate, Davey Smith, George·2017·International Journal of Epidemiology

DOI 10.1093/ije/dyw314PMC PMC5841843

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Abstract

Triangulation is the practice of obtaining more reliable answers to research questions through integrating results from several different approaches, where each approach has different key sources of potential bias that are unrelated to each other. With respect to causal questions in aetiological epidemiology, if the results of different approaches all point to the same conclusion, this strengthens confidence in the finding. This is particularly the case when the key sources of bias of some of the approaches would predict that findings would point in opposite directions if they were due to such biases. Where there are inconsistencies, understanding the key sources of bias of each approach can help to identify what further research is required to address the causal question. The aim of this paper is to illustrate how triangulation might be used to improve causal inference in aetiological epidemiology. We propose a minimum set of criteria for use in triangulation in aetiological epidemiology, summarize the key sources of bias of several approaches and describe how these might be integrated within a triangulation framework. We emphasize the importance of being explicit about the expected direction of bias within each approach, whenever this is possible, and seeking to identify approaches that would be expected to bias the true causal effect in different directions. We also note the importance, when comparing results, of taking account of differences in the duration and timing of exposures. We provide three examples to illustrate these points.

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Written from the paper’s full text.

TL;DR

This paper defines triangulation in aetiological epidemiology as integrating results from several approaches with different, largely unrelated key sources of bias, and proposes minimum criteria for its valid use. It illustrates the framework with three examples—systolic blood pressure and CHD, maternal gestational glucose and birthweight, and breastfeeding and later BMI—showing how explicit bias direction and exposure timing/duration affect causal interpretation.

Key findings

The authors propose five minimum criteria: results from at least two (ideally more) approaches with differing and unrelated key biases are compared; approaches address the same causal question; exposure duration and timing are taken into account; key biases are explicitly acknowledged; and expected bias directions are made explicit where feasible, ideally with some biases in opposite directions.
Table 1 summarizes key sources of bias for conventional and additional approaches, including RCTs, multivariable regression, cross-context comparisons, different control groups, natural experiments, within-sibship comparisons, instrumental variable/Mendelian randomization analyses, and negative control studies. The authors argue that applying multiple statistical methods to the same dataset is not triangulation because key biases such as residual confounding and measurement error remain shared.
Example 1 found lower systolic blood pressure reduced CHD risk across RCT, prospective cohort, and MR approaches, with magnitudes greatest for MR, intermediate for prospective cohorts, and least for RCTs; after accounting for duration of exposure, results were broadly consistent. The example cites 958,074 adults in 61 prospective studies, 25 RCTs with 109,797 participants, and MR data from 22,223 CHD cases and 64,762 controls.
Example 2 found positive associations of maternal fasting glucose with birthweight; IV RCT and MR estimates were both >300 g greater birthweight per 1 mmol/l greater fasting glucose, compared with ∼115 g in fully adjusted multivariable analyses, and the authors concluded more evidence is needed. Example 3 found most approaches, including a large RCT, pointed to no meaningful causal association between breastfeeding and later BMI, while negative control outcomes suggested residual confounding; within-sibship analyses were too small to provide reliable estimates.

Methods

This is a methodological/conceptual paper that proposes a definition and criteria for triangulation, summarizes key biases of several epidemiological approaches in Table 1, and illustrates the framework with three examples.
The examples draw on published meta-analyses, cohort studies, RCTs, and MR studies, plus some de novo analyses, including additional MR sensitivity analyses for systolic blood pressure and an ALSPAC negative control study for breastfeeding.
Comparisons are qualitative: for each example, the authors tabulate key biases and expected directions and consider differences in exposure duration and timing; they do not quantitatively pool results.

Limitations

The paper states that it does not consider how to pool data in a triangulation framework to produce a quantified causal effect estimate, nor does it consider effect modification beyond modification by age when considering timing of exposure; triangulation will mostly provide a qualitative assessment.
The paper states that increased use of triangulation likely requires multidisciplinary collaboration, more extensive data sharing, and additional resources, and that the illustrative examples were chosen to be “messy” rather than proof of principle.
Evident from the text: the examples are selected from the authors’ research interests rather than systematically sampled, some included studies were underpowered (e.g., within-sibship breastfeeding analyses), and judgments about expected bias directions rely on assumptions that may be uncertain.

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Registry

Crossref · 10.1093/ije/dyw314

Cites

81 of 81 references verified0 citation errors · 0 not checkable

Statistics

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Claims the paper makes

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In the paper
“We found positive associations of maternal gestational fasting glucose with offspring birthweight in multivariable regression analyses pooled from European origin cohort studies with minimal adjustment for potential confounders.55”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 2: what is the effect of maternal gestational circulating glucose levels on offspring birthweight?
What the sources say
- Not checked yetcites [55] Tyrrell, 2016, Genetic evidence for causal relationships between maternal obesity-related traits and birth weight, JAMA source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
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In the paper
“but not in the LMIC cohorts.21”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [21] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“but results were attenuated with adjustment for maternal BMI, smoking and socioeconomic position”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [61] Owen, 2005, The effect of breast feeding on mean body mass index throughout the lifecourse; a quantitative review of published and unpublished observational evidence, Am J Clin Nutr source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
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None of the sources cited for this claim have been checked yet.
In the paper
“The largest62,63 of the two within-sibship comparisons suggested that any associations observed between unrelated individuals were explained by shared familial confounding”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [62] Evenhouse, 2005, Improved estimates of the benefits of breastfeeding using sibling comparisons to reduce selection bias, Health Serv Res source
- Not checked yetcites [63] source
Load-bearingStrength:hedged· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“Cross-context comparisons between a UK cohort and pooling of data from five low- or middle-income countries (LMIC) showed an inverse association of duration of breastfeeding with BMI in the UK cohort”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [21] source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“There was an inverse association of ever being exclusively breastfed, and of being exclusively breastfed for at least 8 months, with mean BMI in a meta-analysis of prospective cohort studies with minimal (age and gender) adjustment”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [61] Owen, 2005, The effect of breast feeding on mean body mass index throughout the lifecourse; a quantitative review of published and unpublished observational evidence, Am J Clin Nutr source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“There was an inverse association of ever being exclusively breastfed, and of being exclusively breastfed for at least 8 months, with mean BMI in a meta-analysis of prospective cohort studies with minimal (age and gender) adjustment”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [61] Owen, 2005, The effect of breast feeding on mean body mass index throughout the lifecourse; a quantitative review of published and unpublished observational evidence, Am J Clin Nutr source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
In the paper
“and there was evidence of publication bias.61”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [61] Owen, 2005, The effect of breast feeding on mean body mass index throughout the lifecourse; a quantitative review of published and unpublished observational evidence, Am J Clin Nutr source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes

Show all 59 claims

It may state two separate claims as one. 43
Probably not a substantial claim. 52

In the paper
“Effect estimates from all three approaches point in the same direction of lower SBP causing reduced odds of CHD (Figure 2a), but the magnitudes of this effect vary, being greatest for the MR studies, intermediate for the prospective cohorts and least for the RCTs.53”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 1: what is the causal cumulative effect of lower systolic blood pressure on CHD risk?
What the sources say
- Not checked yetcites [53] source
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Positive fasting glucose-birthweight effects were also seen in the IV of glucose as an intermediate in an RCT57 and IV MR studies.55”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 2: what is the effect of maternal gestational circulating glucose levels on offspring birthweight?
What the sources say
- Not checked yetcites [57] source
- Not checked yetcites [55] Tyrrell, 2016, Genetic evidence for causal relationships between maternal obesity-related traits and birth weight, JAMA source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“though the within-sibship analyses in both studies were imprecisely estimated and not reliably different from the results of analyses between unrelated individuals.62–64”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [62] Evenhouse, 2005, Improved estimates of the benefits of breastfeeding using sibling comparisons to reduce selection bias, Health Serv Res source
- Not checked yetcites [63] source
- Not checked yetcites [64] source
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“In the LMICs, socioeconomic position was not notably related to breastfeeding or related to it in the opposite direction to that seen in the UK cohort.”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [21] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“When we plot the effect of each approach against estimated duration of exposure, as done by Ference et al.,53 the results provide some support for a cumulative duration effect, with a biased (exaggerated) effect from the IV analyses in the RCT (Figure 3b).”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 2: what is the effect of maternal gestational circulating glucose levels on offspring birthweight?
What the sources say
- Not checked yetcites [53] source
Load-bearingStrength:hedged· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- Rests on an assumption: For many exposures, greater duration of exposure results in greater risk.
None of the sources cited for this claim have been checked yet.
In the paper
“The idea behind triangulation is that when we compare different approaches with assumed unrelated sources of bias, particularly if the expected direction of bias for some of the approaches is different, we would not expect to obtain the same estimates of the causal effect (unless all were unbiased).7,15”
§ Triangulation in aetiological epidemiology > Definition of triangulation
What the sources say
- Not checked yetcites [7] source
- Not checked yetcites [15] Lipton P. Inference to the best explanation. In: Newton-Smith WH (ed). A Companion to the Philosophy of Science. New York, NY: Blackwell, 2000.
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence.
- The authors state this as an assumption: The key sources of bias of different approaches used in a triangulation are largely unrelated to each other.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In this case we would want to avoid comparing these two studies in triangulation, as the key source of bias is essentially the same.”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence.
- The authors state this as an assumption: The key sources of bias of different approaches used in a triangulation are largely unrelated to each other.
None of the sources cited for this claim have been checked yet.
In the paper
“In RCTs, failure to compare outcomes by the original randomized groups or subversion of the randomized process by researchers or participants is a likely key source of confounding.”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
What the sources say
- Not checked yetcites [17] source
- Not checked yetcites [18] Furberg CD. How should one analyse and interpret clinical trials in which patients don’t take the treatments assigned to them? James Lind Library Bulletin: Commentaries on the History of Treatment Evaluation . 2009. http://www.jameslindlibrary.org/articles/how-should-one-analyse-and-interpret-clinical-trials-in-which-patients-dont-take-the-treatments-assigned-to-them/ (9 May 2016, date last accessed). source
Load-bearingStrength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but if the source of violation of this assumption is different, and unrelated, in each of these approaches it would be appropriate to triangulate them”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
Load-bearingStrength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence.
- The authors state this as an assumption: The key sources of bias of different approaches used in a triangulation are largely unrelated to each other.
None of the sources cited for this claim have been checked yet.
In the paper
“Because MR is relatively novel there has been considerable focus on its limitations and methods for testing and dealing with these, in particular for dealing with violation of the exclusion restriction criteria due to genuine pleiotropy.36,37”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
What the sources say
- Not checked yetcites [36] source
- Not checked yetcites [37] source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Two of these methods in particular (MR-Egger36 and the weighted median estimator37) are likely to become increasingly used in MR sensitivity analyses, as two-sample MR using summary genome-wide association studies (GWAS) data becomes more common.38”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
What the sources say
- Not checked yetcites [36] source
- Not checked yetcites [37] source
- Not checked yetcites [38] source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Although these approaches have been developed for use in MR, and in particular in the case of MR-Egger for two-sample MR,36,38 they could clearly be used as a sensitivity analysis to assess violation of the exclusion restriction criteria when several different treatment IVs are used in RCTs.”
§ Triangulation in aetiological epidemiology > What we mean by unrelated sources of bias
What the sources say
- Not checked yetcites [36] source
- Not checked yetcites [38] source
Load-bearingStrength:boosted· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
In the paper
“The MR study will likely reflect mean differences in maternal glucose levels across the whole of pregnancy, whereas the multivariable regression analyses in the pregnancy cohorts are based on one (baseline) measure of fasting glucose taken between 26 and 28 weeks of gestation and thus reflect lower glucose during the last 12–14 weeks of gestation (assuming, as in Ference et al.,53 that exposure”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 2: what is the effect of maternal gestational circulating glucose levels on offspring birthweight?
What the sources say
- Not checked yetcites [53] source
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- The authors state this as an assumption: In prospective cohort studies, exposure duration is from baseline assessment to end of follow-up.
None of the sources cited for this claim have been checked yet.
In the paper
“Whether using a triangulation approach or not, if the timing of exposure is central to the research question then that needs to be specifically included in the hypotheses tested and the comparisons made.49,50”
§ Triangulation in aetiological epidemiology > Duration and timing of exposure being assessed with different approaches
What the sources say
- Not checked yetcites [49] source
- Not checked yetcites [50] Smith ADAC Hardy R Heron J . Model selection of life course hypotheses involving continuous exposures. Int J Epidemiolernational Journal of Epidemiology 2016, July 1. pii: dyw164. [Epub ahead of print.]. source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
In the paper
“In cross-context comparisons, results are compared between two or more different populations, or the same population in two or more different contexts (e.g. over different time periods), under the a priori assumption that confounding structures differ between the different contexts/populations.21”
§ Triangulation in aetiological epidemiology > Different directions of bias within the approaches being compared
What the sources say
- Not checked yetcites [21] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
- The authors state this as an assumption: Confounding structures differ between the different contexts/populations in cross-context comparisons.
None of the sources cited for this claim have been checked yet.
In the paper
“Using different controls in case control studies22 has similar underlying principles to those described above for cross-context studies, but here differences in exposure misclassification (rather than confounding structures) is the most common use of this approach.22–24”
§ Triangulation in aetiological epidemiology > Different directions of bias within the approaches being compared
What the sources say
- Not checked yetcites [22] Smith, 1988, Cancer case-control studies with other cancers as controls, Int J Epidemiol source
- Not checked yetcites [23] source
- Not checked yetcites [24] source
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The idea with this approach is that where there are concerns about the association being driven by residual confounding in one context/population, additional populations are sought in which the confounders of concern are unrelated to exposure or outcome (or related in the opposite direction) or do not vary in the additional populations. Thus, in the additional populations there would either be no”
§ Triangulation in aetiological epidemiology > Different directions of bias within the approaches being compared
Load-bearingStrength:hedged· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim. It may state two separate claims as one.
- The authors state this as an assumption: Confounding structures differ between the different contexts/populations in cross-context comparisons.
None of the sources cited for this claim have been checked yet.
In the paper
“Three studies were identified which examined associations of being breastfed with BMI.”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [62] Evenhouse, 2005, Improved estimates of the benefits of breastfeeding using sibling comparisons to reduce selection bias, Health Serv Res source
- Not checked yetcites [63] source
- Not checked yetcites [64] source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
None of the sources cited for this claim have been checked yet.
In the paper
“An intention- to-treat analysis was used to assess the impact of these differences on BMI at age 6.5 (n = 13 879) and on BMI and fat mass index (FMI) at age 11.5 (n = 13 866).”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [65] Kramer, 2007, Long-term effects of prolonged and exclusive breastfeeding on child height, weight, adiposity, and blood pressure: new evidence from a large randomized trial, Am J Clin Nutr source
- Not checked yetcites [66] source
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibratedn = 13,879
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
In the paper
“We explored associations of observed confounders with these outcomes and these verified that these were appropriate negative controls (Table S3)”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites Table S3
Load-bearingStrength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The intervention resulted in marked differences in ever breastfeeding, duration of breastfeeding and whether breastfeeding was exclusive.”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [65] Kramer, 2007, Long-term effects of prolonged and exclusive breastfeeding on child height, weight, adiposity, and blood pressure: new evidence from a large randomized trial, Am J Clin Nutr source
- Not checked yetcites [66] source
Load-bearingStrength:plain· not yet calibratedCausal:direct· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
In the paper
“It was demonstrated that SEP did not relate to breastfeeding in the LMICs or was in the opposite direction (more affluent and educated women being less likely to breastfeed) to that seen in the UK (breastfeeding more common in the more affluent and educated women). If an association in UK participants was due to SEP confounding, we would expect a null or opposite direction association in those”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [21] source
Load-bearingStrength:boosted· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
It may state two separate claims as one.
- The authors state this as an assumption: Confounding structures differ between the different contexts/populations in cross-context comparisons.
None of the sources cited for this claim have been checked yet.
In the paper
“Two of these used the same data from the US National Longitudinal Study of Adolescent Health (Add Health) and had similar results;62,63”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [62] Evenhouse, 2005, Improved estimates of the benefits of breastfeeding using sibling comparisons to reduce selection bias, Health Serv Res source
- Not checked yetcites [63] source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
In the paper
“The third study was also US-based and included 488 sib-pairs whose BMI was assessed at ages 9–19 years64”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [64] source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“We tried to identify outcomes in the ALSPAC cohort67,68 that would be affected by confounders that are important in the relation of breastfeeding to later offspring outcomes but for which a biological/causal effect of breastfeeding was unlikely”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [67] source
- Not checked yetcites [68] source
Load-bearingStrength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
Probably not a substantial claim.
In the paper
“we present results from the study that focused on mean BMI (2734 sib-pairs; ages 12–18).62”
§ Triangulation in aetiological epidemiology > Three illustrative examples of the use of triangulation in aetiological epidemiology > Example 3: What is the causal effect of having been breastfed on later body mass index?
What the sources say
- Not checked yetcites [62] Evenhouse, 2005, Improved estimates of the benefits of breastfeeding using sibling comparisons to reduce selection bias, Health Serv Res source
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“The process for conducting International Agency for Research on Cancer (IARC) monographs also has some similarities to triangulation, in that the aim is to identify and review ‘all relevant papers on cancer in humans and experimental animals …’74”
§ Triangulation in aetiological epidemiology > Concluding remarks
What the sources say
- Not checked yetcites [74] Pearce, 2015, IARC Monographs: 40 years of evaluating carcinogenic hazards to humans, Environ Health Perspect source
Strength:plain· not yet calibratedCausal:correlational· not yet calibrated
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“Cross-context comparisons with many different populations each with different confounding structures, RCTs of intermediates that use multiple different treatments (for example different classes of antihypertensives to test the effect of blood pressure on CHD31) and MR with multiple genetic instruments that relate to the exposure of interest through different mechanisms, might be considered in this”
§ Triangulation in aetiological epidemiology > Concluding remarks
What the sources say
- Not checked yetcites [31] source
Strength:hedged· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Our definition and criteria for triangulation are similar to some other approaches to causality, including Hill’s concept of consistency16 and work by Susser,69,70 Morris71 and more recently Shipley,72”
§ Triangulation in aetiological epidemiology > Concluding remarks
What the sources say
- Not checked yetcites [16] Hill, 1965, The environment and disease: association or causation, Proc R Soc Med
- Not checked yetcites [69] Susser M. Procedures for establishing causal association. In: Stewart GT (ed). Trends in Epidemiology:Application to Health Service Research and Training. Springfield, IL: C.C. Thomas, 1972.
- Not checked yetcites [70] Susser M. Causal Thinking in the Health Sciences. Concepts and Strategies in Epidemiology. New York, NY: Oxford University Press, 1973.
- Not checked yetcites [71] Morris JN. Uses of Epidemiology. 2nd edn. Edinburgh, UK: Livingstone, 1964.
Strength:plain· not yet calibrated
Tests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passes
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.

78 of 78

The authors state this as an assumption.
The key sources of bias of different approaches used in a triangulation are largely unrelated to each other.
“where each approach has different (and assumed to be largely unrelated) key sources of potential bias.”
The authors state this as an assumption.
In a triangulation using multivariable regression, RCT, and MR, bias from unmeasured confounders, failure to examine effects in randomized groups or subversion of randomization, and population stratification are not related to each other.
“we would assume in a triangulation study using these approaches that bias from unmeasured confounders, failure to examine effects in randomized groups or subversion of the randomization, and population stratification in an MR study would not be related to each other.”
The authors state this as an assumption.
The negative control exposure does not have a causal effect on the real outcome.
“The negative control exposure does not have a causal effect on the real outcome.”
The authors state this as an assumption.
The key sources of bias, including specific confounders, misclassification bias and other biases, are the same for the real and negative control exposures.
“The key sources of bias, including specific confounders, misclassification bias and other biases, are the same for the real and negative control exposures.”
The authors state this as an assumption.
The IV is not associated with confounders of the exposure-outcome association.
“IV is not associated with confounders of exposure-outcome association.”
The authors state this as an assumption.
The IV is not related to the outcome other than via its association with the exposure (the exclusion restriction criteria).
“IV is not related to the outcome other than via its association with the exposure (the exclusion restriction criteria)”
The authors state this as an assumption.
The populations being compared in natural experiments are similar with the exception of the naturally or quasi-randomized exposure.
“The populations being compared are similar with the exception of the naturally or quasi-randomized exposure”
The authors state this as an assumption.
There is little or no individual-level confounding in within sibling comparisons.
“There is little or no individual-level confounding.”
The authors state this as an assumption.
Different results between populations in cross-context comparisons are due to different confounding structures and not due to true differences in causal effects between populations.
“Different results between populations are due to different confounding structures and not due to true differences in causal effects between populations.”
The authors state this as an assumption.
Similar results between populations cannot be explained by confounding, given the differences between the populations/contexts in their confounding structures.
“Similar results between populations cannot be explained by confounding, given the differences between the populations/contexts in their confounding structures.”
The authors state this as an assumption.
There are no other (than confounding) sources of bias that could explain similar or different results between the two populations.
“There are no other (than confounding) sources of bias that could explain similar or different results between the two populations”
The authors state this as an assumption.
The different sources of bias for the different control groups are different and would produce different results.
“The different sources of bias for the different control groups are different and would produce different results”
The authors state this as an assumption.
Confounding structures differ between the different contexts/populations in cross-context comparisons.
“under the a priori assumption that confounding structures differ between the different contexts/populations.21”
The paper relies on this without support.
For many exposures, greater duration of exposure results in greater risk.
“For many exposures, greater duration of exposure will result in greater risk.”
The authors state this as an assumption.
In prospective cohort studies, exposure duration is from baseline assessment to end of follow-up.
“(assuming, as in Ference et al.,53 that exposure duration in a prospective cohort is from baseline assessment to end of follow-up)”

15 of 15

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

Blood-pressure discussion points to the glucose table
Minor
The cited Table 3 covers maternal glucose and birthweight, not the systolic blood pressure and coronary heart disease results discussed in this sentence.
Pregnancy exposure is described in months instead of weeks
Minor
The same discussion says the cohort measurements represent exposure during the last 12-14 weeks of gestation, but later calls that interval the last 12-14 months of pregnancy.

2 of 2

Risks and Benefits of Estrogen Plus Progestin in Healthy Postmenopausal Women: Principal Results From the Women's Health Initiative Randomized Controlled Trial
Writing Group for the Women's Health Initiative Investigators (2002) · JAMA: The Journal of the American Medical Association · doi:10.1001/jama.288.3.321 · Cited by 15,889
Age-specific relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies
(2002) · The Lancet · doi:10.1016/s0140-6736(02)11911-8 · Cited by 10,266
Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression
Bowden, J., Davey Smith, G., & Burgess, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv080 · Cited by 9,816
Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator
Bowden, J., Davey Smith, G., Haycock, P. C., & Burgess, S. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21965 · Cited by 8,922
The environment and disease: association or causation?
Hill, A. B. (2015) · Journal of the Royal Society of Medicine · doi:10.1177/0141076814562718 · Cited by 7,804
The Environment and Disease: Association or Causation?
Hill, A. B. (1965) · Proceedings of the Royal Society of Medicine · doi:10.1177/003591576505800503 · Cited by 6,974
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,536
SAGE Handbook of Mixed Methods in Social & Behavioral Research
Tashakkori, A., & Teddlie, C. (2010) · doi:10.4135/9781506335193 · Cited by 6,215
‘Mendelian randomization’: can genetic epidemiology contribute to understanding environmental determinants of disease?*
Davey Smith, G., & Ebrahim, S. (2003) · International Journal of Epidemiology · doi:10.1093/ije/dyg070 · Cited by 5,882
Mendelian randomization: genetic anchors for causal inference in epidemiological studies
Davey Smith, G., & Hemani, G. (2014) · Human Molecular Genetics · doi:10.1093/hmg/ddu328 · Cited by 4,677

Flagged first, then by citations

Show all 81 references

Research works printed but not linked: 2

Vanderweele TJ. Explanation in Causal Inference. New York, NY: Oxford University Press, 2015.
Rothman KJ Greenland S Lash TL. Modern Epidemiology. 3d edn. Philadelphia, PA: Lippincott Williams & Wilkins, 2008.

Sorted by citations

Items 101–200 of 998

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Self-citation
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Self-citation
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Self-citation
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Self-citation
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Self-citation
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Lyall, D. M., Kormilitzin, A., Lancaster, C. L., Sousa, J., Petermann-Rocha, F. E., Buckley, C., Harshfield, E. L., Iveson, M. H., Madan, C. R., Ardle, R. M., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13391 · Cited by 74
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Carnegie, R., Zheng, J., Sallis, H., Jones, H. J., Wade, K. H., Evans, J., Zammit, S., Munafo, M. R., & Martin, R. M. (2019) · The Lancet Psychiatry · doi:10.1016/s2215-0366(19)30293-7 · Cited by 69
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Vandenbroucke, J. P., Brickley, E. B., Vandenbroucke‐Grauls, C. M. J. E., & Pearce, N. E. (2020) · Epidemiology · doi:10.1097/ede.0000000000001251 · Cited by 62
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Sadik, A., Dardani, C., Pagoni, P., Havdahl, A., Stergiakouli, E., The iPSYCH Autism Spectrum Disorder Working Group, Grove, J., Khandaker, G. M., Sullivan, S. A., Zammit, S., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01845-9 · Cited by 71
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Pembrey, L., Barreto, M. L., Douwes, J. E., Cooper, P. J., Henderson, J., Mpairwe, H., Ardura‐Garcia, C., Chico, M. E., Brooks, C., Cruz, Á. A., et al. (2018) · ERJ Open Research · doi:10.1183/23120541.00013-2018 · Cited by 70
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Authors: Lawlor, D. A., Tilling, K., & Davey Smith, G.
Affiliations: University of Bristol (GB)
Corresponding author: Lawlor, Debbie A.
ORCID: Lawlor, Debbie A.
Tilling, Kate
Venue: International Journal of Epidemiology
Publisher: Oxford University Press (OUP)
Published: 2017-01-20
Volume: 45
Issue: 6
Pages: dyw314
ISSN: 0300-5771 (ISSN-L)
1464-3685
DOI: 10.1093/ije/dyw314
PMC: PMC5841843
OpenAlex: W2579779633
Semantic Scholar: 8ed819773b0e480d384cecb6fa7b55c5eb1c55de
Cited by: 1,129
References: 81
Citations by year: 2026: 71 · 2025: 177 · 2024: 153 · 2023: 154 · 2022: 131 · 2021: 124 · 2020: 124 · 2019: 107 · 2018: 60 · 2017: 24
FWCI: 65.73
Type: journal article
Language: en
Open access: bronze
doi.org
Locations: International Journal of Epidemiology (publishedVersion)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Explore Bristol Research (publishedVersion)
Funders: Harvard University
National Institute for Health and Care Research (NF-SI-0611-10196)
European Commission (2007–2013, FP7 2007-2013, FP7/2007, 669545)
University of Bristol (MC_UU_12013/1, MC_UU_12013/9, MC_UU_12013/5)
Universiteit Leiden
Medical Research Council (MC_UU_12013_5, MC_UU_12013, MCUU12013/1, MC_PC_15018, MC_UU_12013/9)
H2020 European Research Council (669545)
Topics: Physical Sciences › Mathematics › Statistics and Probability › Advanced Causal Inference Techniques
Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
Keywords: triangulation, causal inference, bias, confounding, causal effect estimation, exposure duration, exposure timing, research design comparison
MeSH terms: Data Collection, Epidemiology, Humans, Research Design, Bias, Causality, Mendelian Randomization Analysis

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Triangulation in aetiological epidemiology

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